def _transfer_from_same_file(self, goa, arch_file):
""" Transfer function from same architecture file
"""
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree,
goa,
confidence=confidence,
min_count=1,
correction=self.options.correction)
cov = self.options.minimum_coverage / 100.0
self.log.info("Transferring function from same file. Min coverage=%s",
str(cov))
all_annotated = frozenset(goa.left.keys())
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
out = open(arch_file_name, "w")
# for each architecture and its associated proteins...
carriage_return = os.linesep
twocr = os.linesep * 2
self.log.info("Arch file: %s", arch_file)
for arch, prots in ArchReader(arch_file, cov):
if not prots or arch == "NO_ASSIGNMENT":
# if there is no annotation for proteins in the arch...
if self.options.results_by_protein:
print("{}{}".format(twocr.join(prots), carriage_return))
if self.options.results_by_arch:
out.write("{}{}".format(arch, twocr))
continue
targets = set(prots)
annotated_prots = targets & all_annotated
lines = carriage_return.join([
" %.4f: %s (%s)" % (p_value, term.id, term.name)
for term, p_value in ora.test_group(targets)
])
if self.options.results_by_arch:
out.write("{}{}{}{}".format(arch, carriage_return, lines,
twocr))
if self.options.results_by_protein:
for rest, prot in i_zip(combinations(prots,
len(prots) - 1),
reversed(prots)):
print(prot)
if prot in annotated_prots:
for term, p_value in ora.test_group(rest):
print(" %.4f: %s (%s)" %
(p_value, term.id, term.name))
else:
print(lines)
print()
if self.options.results_by_arch:
out.close()
self._filter_arch_file(arch_file_name,
self.options.results_by_arch)
def _transfer_from_same_file(self, goa, arch_file):
""" Transfer function from same architecture file
"""
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree, goa,
confidence=confidence,
min_count=1,
correction=self.options.correction)
cov = self.options.minimum_coverage / 100.0
self.log.info("Transferring function from same file. Min coverage=%s",
str(cov))
all_annotated = frozenset(goa.left.keys())
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
out = open(arch_file_name, "w")
# for each architecture and its associated proteins...
carriage_return = os.linesep
twocr = os.linesep * 2
self.log.info("Arch file: %s", arch_file)
for arch, prots in ArchReader(arch_file, cov):
if not prots or arch == "NO_ASSIGNMENT":
# if there is no annotation for proteins in the arch...
if self.options.results_by_protein:
print("{}{}".format(twocr.join(prots), carriage_return))
if self.options.results_by_arch:
out.write("{}{}".format(arch, twocr))
continue
targets = set(prots)
annotated_prots = targets & all_annotated
lines = carriage_return.join([" %.4f: %s (%s)" %
(p_value, term.id, term.name)
for term, p_value in
ora.test_group(targets)])
if self.options.results_by_arch:
out.write("{}{}{}{}".format(arch, carriage_return,
lines, twocr))
if self.options.results_by_protein:
for rest, prot in i_zip(combinations(prots, len(prots)-1),
reversed(prots)):
print(prot)
if prot in annotated_prots:
for term, p_value in ora.test_group(rest):
print(" %.4f: %s (%s)" % (p_value,
term.id,
term.name))
else:
print(lines)
print()
if self.options.results_by_arch:
out.close()
self._filter_arch_file(arch_file_name,
self.options.results_by_arch)
def _transfer_from_both(self, goa, arch_target, arch_source):
""" Transfer from both an external file and the same file using a single GOA
file
"""
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value. We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree, goa,
confidence=confidence,
min_count=1, correction='None')
cov = self.options.minimum_coverage / 100.0
self.log.info("Transferring function from both files. Min coverage=" + str(cov))
all_annotated = frozenset(goa.left.keys()) # all annotated proteins
prots_per_arch = dict()
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
out = open(arch_file_name, "w")
self.log.info("\t Source architecture: " + arch_source)
self.log.info("\t Target (and source, as well) architecture: " + arch_target)
for arch, prots in ArchReader(arch_source, cov):
prots_per_arch[arch] = prots
for arch, prots in ArchReader(arch_target, cov):
other_prots = set()
if arch in prots_per_arch:
other_prots = prots_per_arch[arch]
targets = set(other_prots) | set(prots)
annotated_prots = targets & all_annotated
if not annotated_prots or arch == "NO_ASSIGNMENT":
# if there is no annotation for proteins in the arch...
if self.options.results_by_protein:
print (os.linesep * 2).join(prots), os.linesep
if self.options.results_by_arch:
out.write(arch + "\n\n")
continue
lines = os.linesep.join([" %.4f: %s (%s)" %
(p_value, term.id, term.name)
for term, p_value in ora.test_group(targets)])
if self.options.results_by_arch:
out.write(arch + "\n")
out.write(lines)
out.write("\n")
if self.options.results_by_protein:
for prot in prots:
print prot
if prot in annotated_prots:
for term, p_value in ora.test_group(targets - set([prot])):
print " %.4f: %s (%s)" % (p_value, term.id, term.name)
else:
print lines
print
if self.options.results_by_arch:
out.close()
self._filter_arch_file(arch_file_name, self.options.results_by_arch)
def _transfer_from_other_file(self, goa, arch_target, arch_source):
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree,
goa,
confidence=confidence,
min_count=1,
correction='None')
cov = self.options.minimum_coverage / 100.0
goterms = dict()
for arch, prots in ArchReader(arch_source, cov):
if arch != "NO_ASSIGNMENT":
goterms[arch] = ora.test_group(prots)
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
with open(arch_file_name, "w") as out:
for arch in sorted(goterms.keys()):
out.write(arch + "\n")
for term, p_value in goterms[arch]:
line = " %.4f: %s (%s)" % (p_value, term.id,
term.name)
out.write(line)
out.write("\n")
out.write("\n")
if self.options.results_by_protein:
for arch, prots in ArchReader(arch_target, cov):
if arch in goterms:
for prot in prots:
print(prot)
for term, p_value in goterms[arch]:
print(" %.4f: %s (%s)" %
(p_value, term.id, term.name))
print()
else:
for prot in prots:
print(prot)
print()
if self.options.results_by_arch:
self._filter_arch_file(arch_file_name,
self.options.results_by_arch)
def _transfer_from_other_file(self, goa, arch_target, arch_source):
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree, goa,
confidence=confidence,
min_count=1, correction='None')
cov = self.options.minimum_coverage / 100.0
goterms = dict()
for arch, prots in ArchReader(arch_source, cov):
if arch != "NO_ASSIGNMENT":
goterms[arch] = ora.test_group(prots)
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
with open(arch_file_name, "w") as out:
for arch in sorted(goterms.keys()):
out.write(arch + "\n")
for term, p_value in goterms[arch]:
line = " %.4f: %s (%s)" % (p_value,
term.id,
term.name)
out.write(line)
out.write("\n")
out.write("\n")
if self.options.results_by_protein:
for arch, prots in ArchReader(arch_target, cov):
if arch in goterms:
for prot in prots:
print(prot)
for term, p_value in goterms[arch]:
print(" %.4f: %s (%s)" % (p_value,
term.id,
term.name))
print()
else:
for prot in prots:
print(prot)
print()
if self.options.results_by_arch:
self._filter_arch_file(arch_file_name,
self.options.results_by_arch)
def process_file(self, input_file):
"""Processes the given input file that contains the domain
architectures."""
self.log.info("Running overrepresentation analysis")
self.log.info("p-value = %.4f, correction method = %s" % \
(self.options.confidence, self.options.correction))
overrep = OverrepresentationAnalyser(self.go_tree, self.go_mapping,
confidence = self.options.confidence,
min_count = self.options.min_size,
correction = self.options.correction)
cache = {}
num_no_annotations = 0
num_no_domains = 0
total_seqs = 0
for line in open_anything(input_file):
parts = line.strip().split("\t")
gene_id, arch = parts[0], tuple(parts[2].split(";"))
total_seqs += 1
if arch == ("NO_ASSIGNMENT", ):
num_no_domains += 1
num_no_annotations += 1
continue
if arch not in cache:
cache[arch] = overrep.test_group(arch)
print gene_id
for term, p_value in cache[arch]:
print " %.4f: %s (%s)" % (p_value, term.id, term.name)
print
if len(cache[arch]) == 0:
num_no_annotations += 1
self.log.info("Total number of sequences processed: %d" % total_seqs)
if num_no_annotations:
self.log.info("%d sequences have no overrepresented annotations :("
% num_no_annotations)
if num_no_domains:
self.log.info("%d sequences have no domains at all :(" % num_no_domains)
def process_file(self, input_file):
"""Processes the given input file that contains the domain
architectures."""
self.log.info("Running overrepresentation analysis")
self.log.info("p-value = %.4f, correction method = %s",
self.options.confidence, self.options.correction)
if self.options.arch_file:
arch_file_name = self.options.arch_file
if self.options.ignore:
arch_file_name += "_unfiltered"
arch_file = open(arch_file_name, "w")
confidence = self.options.confidence
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
overrep = OverrepresentationAnalyser(self.go_tree,
self.go_mapping,
confidence=confidence,
min_count=self.options.min_size,
correction=self.options.
correction)
cache = {}
num_no_annotations = 0
num_no_domains = 0
total_seqs = 0
for line in open_anything(input_file):
parts = line.strip().split("\t")
gene_id = parts[0]
prts = parts[3].replace("{", ";").replace("}", ";").split(";")
arch = tuple([x for x in prts if x])
total_seqs += 1
if arch == ("NO_ASSIGNMENT", ):
num_no_domains += 1
num_no_annotations += 1
continue
if arch not in cache:
cache[arch] = overrep.test_group(arch)
if self.options.arch_file:
arch_file.write("{}\n".format(parts[3])) # architecture
for term, p_value in cache[arch]:
line = " %.4f: %s (%s)\n" % (p_value, term.id,
term.name)
arch_file.write(line)
arch_file.write("\n")
if self.options.results_by_protein:
print(gene_id)
for term, p_value in cache[arch]:
print(" %.4f: %s (%s)" % (p_value, term.id, term.name))
print()
if not cache[arch]:
num_no_annotations += 1
self.log.info("Total number of sequences processed: %d", total_seqs)
if num_no_annotations:
self.log.info("%d sequences have no overrepresented annots. :(",
num_no_annotations)
if num_no_domains:
self.log.info("%d sequences have no domains at all :(",
num_no_domains)
if self.options.arch_file:
arch_file.close()
if self.options.ignore:
# we filter the file with the significance value
filterer = ResultFileFilter(arch_file_name)
filterer.filter(self.options.arch_file,
confidence=self.options.confidence)
def _transfer_from_both(self, goa, arch_target, arch_source):
""" Transfer from both an external file and the same file using a single GOA
file
"""
confidence = self.options.max_pvalue
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value. "
"We will filter results later.")
ora = OverrepresentationAnalyser(self.go_tree,
goa,
confidence=confidence,
min_count=1,
correction='None')
cov = self.options.minimum_coverage / 100.0
self.log.info(
"Transferring function from both files."
" Min coverage=%s", str(cov))
all_annotated = frozenset(goa.left.keys()) # all annotated proteins
prots_per_arch = dict()
if self.options.results_by_arch:
arch_file_name = self.options.results_by_arch
if self.options.ignore:
arch_file_name += "_unfiltered"
out = open(arch_file_name, "w")
self.log.info("\t Source architecture: %s", arch_source)
self.log.info("\t Target (and source, as well) architecture: %s",
arch_target)
for arch, prots in ArchReader(arch_source, cov):
prots_per_arch[arch] = prots
for arch, prots in ArchReader(arch_target, cov):
other_prots = set()
if arch in prots_per_arch:
other_prots = prots_per_arch[arch]
targets = set(other_prots) | set(prots)
annotated_prots = targets & all_annotated
if not annotated_prots or arch == "NO_ASSIGNMENT":
# if there is no annotation for proteins in the arch...
if self.options.results_by_protein:
print((os.linesep * 2).join(prots))
print()
if self.options.results_by_arch:
out.write(arch + "\n\n")
continue
lines = os.linesep.join([
" %.4f: %s (%s)" % (p_value, term.id, term.name)
for term, p_value in ora.test_group(targets)
])
if self.options.results_by_arch:
out.write(arch + "\n")
out.write(lines)
out.write("\n")
if self.options.results_by_protein:
for prot in prots:
print(prot)
if prot in annotated_prots:
grp = targets - set([prot])
for term, p_value in ora.test_group(grp):
print(" %.4f: %s (%s)" %
(p_value, term.id, term.name))
else:
print(lines)
print()
if self.options.results_by_arch:
out.close()
self._filter_arch_file(arch_file_name,
self.options.results_by_arch)
def process_file(self, input_file):
"""Processes the given input file that contains the domain
architectures."""
self.log.info("Running overrepresentation analysis")
self.log.info("p-value = %.4f, correction method = %s",
self.options.confidence, self.options.correction)
if self.options.arch_file:
arch_file_name = self.options.arch_file
if self.options.ignore:
arch_file_name += "_unfiltered"
arch_file = open(arch_file_name, "w")
confidence = self.options.confidence
if self.options.ignore:
confidence = float("inf")
self.log.info("Ignored the significance value."
" We will filter results later.")
overrep = OverrepresentationAnalyser(
self.go_tree,
self.go_mapping,
confidence=confidence,
min_count=self.options.min_size,
correction=self.options.correction)
cache = {}
num_no_annotations = 0
num_no_domains = 0
total_seqs = 0
for line in open_anything(input_file):
parts = line.strip().split("\t")
gene_id = parts[0]
prts = parts[3].replace("{", ";").replace("}", ";").split(";")
arch = tuple([x for x in prts if x])
total_seqs += 1
if arch == ("NO_ASSIGNMENT", ):
num_no_domains += 1
num_no_annotations += 1
continue
if arch not in cache:
cache[arch] = overrep.test_group(arch)
if self.options.arch_file:
arch_file.write("{}\n".format(parts[3])) # architecture
for term, p_value in cache[arch]:
line = " %.4f: %s (%s)\n" % (p_value, term.id,
term.name)
arch_file.write(line)
arch_file.write("\n")
if self.options.results_by_protein:
print(gene_id)
for term, p_value in cache[arch]:
print(" %.4f: %s (%s)" % (p_value, term.id, term.name))
print()
if not cache[arch]:
num_no_annotations += 1
self.log.info("Total number of sequences processed: %d", total_seqs)
if num_no_annotations:
self.log.info("%d sequences have no overrepresented annots. :(",
num_no_annotations)
if num_no_domains:
self.log.info("%d sequences have no domains at all :(",
num_no_domains)
if self.options.arch_file:
arch_file.close()
if self.options.ignore:
# we filter the file with the significance value
filterer = ResultFileFilter(arch_file_name)
filterer.filter(self.options.arch_file,
confidence=self.options.confidence)